- classmethod RandomEffects.from_formula(formula, data, *, weights=None, check_rank=True)¶
Create a model from a formula
Formula to transform into model. Conforms to formulaic formula rules.
Data structure that can be coerced into a PanelData. In most cases, this should be a multi-index DataFrame where the level 0 index contains the entities and the level 1 contains the time.
- weights: array_like
Weights to use in estimation. Assumes residual variance is proportional to inverse of weight to that the residual times the weight should be homoskedastic.
Flag indicating whether to perform a rank check on the exogenous variables to ensure that the model is identified. Skipping this check can reduce the time required to validate a model specification. Results may be numerically unstable if this check is skipped and the matrix is not full rank.
Model specified using the formula
Unlike standard formula syntax, it is necessary to explicitly include a constant using the constant indicator (1)
>>> from linearmodels import RandomEffects >>> from linearmodels.panel import generate_panel_data >>> panel_data = generate_panel_data() >>> mod = RandomEffects.from_formula("y ~ 1 + x1", panel_data.data) >>> res = mod.fit()
- Return type